Description: We use the ASM General Meeting Program Book to help students learn about the diversity of microbiology subdisciplines, organization of scientific meetings, and how to build concept maps.
Pedagogy Keywords: Assess, Learn, Teach
Core Themes: Theme 6: Teaching and Learning
Microbial Theme(s) Addressed: Integrating themes
Microorganisms: N/A
Keywords: Concept maps
Intended Audience:
Microbiology/Biology majors
Allied health majors
Biotechnology majors
Science education majors
Nonmajors
Learning Time:
1 hour in Day 1
1.5 hours in Day 2 (outside of class)
30 minutes in Day 3
ACTIVITY
Learning Objectives.
At completion of this exercise, students will:
1. demonstrate understanding of the breadth of subdisciplines of microbiology.
2. demonstrate understanding of the nature of study within the subdisciplines of microbiology.
3. demonstrate understanding of the interrelationships between the subdisciplines of microbiology through the development of concept maps.
4. apply knowledge of the organization of scientific meetings through the creation of a conference itinerary.
Ultimately, we hope students will apply the practice of concept mapping to synthesize new information in their future studies.
Background.
This activity was designed with the students in the first microbiology course in mind and is preferably introduced to them in the first few days of the semester. While these students have some general background in biology, this prior knowledge is not essential for students to successfully complete the activity.
PROCEDURE
Materials.
· Preactivity questionnaire, activity worksheet, and postactivity questionnaire (see Student Version)
· Program Books from ASM General Meeting (1)
Student Version.
Student Version
Instructor Version.
Day 1. Concept map overview and preactivity questionnaire
· Students learn the basics of concept mapping and construct a map as a class and in small groups. Students also learn about scientific meetings and how to use the ASM 107th General Meeting Program Book. The activity can take 40 to 50 minutes or longer depending on the instructor (see Lecture notes and Flowchart and rubric for scoring student concept maps).
· For additional information about concept mapping practice and theory see: http://cmap.ihmc.us/Publications/ResearchPapers/TheoryCmaps/TheoryUnderlyingConceptMaps.htm
Day 2 (any day between day 1 and day 3). Activity
· Students go to the library to get a copy of the reserved ASM General Meeting Program Book. Four Program Books were collected during the ASM General Meeting and placed in the campus library on reserve for students to check out (each student was allowed to use the book for 2 hours each time it was checked out).
· Students were asked to record the amount of time spent on this exercise.
· Students generated an itinerary for four conference days, listing the time, title, and presenters of each seminar on the activity worksheet. Students were asked to focus on consortia and colloquia and ignore the poster presentations.
Day 3 (one week after day 1). Postactivity questionnaire (selected questions)
· Students completed a postactivity questionnaire.
Anytime after day 3. Follow-up discussion
Rather than devoting an entire meeting period to activity follow-up discussion, we took advantage of wait time during laboratory periods that involved gel electrophoresis, enzymatic reactions, centrifugation, etc., to discuss students’ postactivity lists and concept maps. We asked students to share the research questions that surprised them the most. Then students examined and reviewed each other’s postactivity concept maps. The students seemed eager to know more about the divisions and the research involved in each after the instructors explained that the 27 ASM Divisions were used as the “key” to score their lists. Most frequently, they wanted to know what they omitted.
Safety Issues.
None
Suggestions for Determining Student Learning.
Rationale and assessment. To assess whether students had become more aware of the subdisciplines of microbiology after this activity, two methods were used: listing subdisciplines of microbiology and constructing a concept map that incorporates the subdisciplines. Concept maps were used in an attempt to bridge the possible gap between the ability to simply list subdisciplines of microbiology and having a true understanding of interrelationships in the field.
Students’ generated itinerary. Students received points for successfully completing the virtual meeting itinerary.
Students’ self-reporting responses. Students’ responses to this activity are solicited in the postactivity questionnaire.
Assessing the listing methods. The 27 divisions of ASM were used as the key or standard list of subdisciplines of microbiology. Because most of the undergraduate biology majors do not necessarily know the differences between DNA viruses (Division S) and RNA viruses (Division T), we combined these two divisions into one.
Statistical analyses. A paired t test was performed to determine the impact of the activity on students’ awareness of the subdisciplines of microbiology. A Pearson correlation was used to investigate the relationship between the listing and concept map methods.
Scoring the concept maps (optional). There are many ways to score concept maps with advantages and disadvantages associated with each approach. We combined the relational scoring method and the holistic approach (2, 3). We scored the total number of concepts used in the map and the legitimate links between concepts. Instead of scoring the hierarchies of distinct levels, we scored the holistic value (from 1 to 3, with 3 being the most accurate and comprehensive) denoting the overall quality of the map. We did not take points off for invalid or incorrect information because it was very rare.
Flowchart and rubric for scoring student concept maps
To meet the objectives of this activity, the scoring of the concept maps is not essential. We scored the maps to see whether there was a correlation between the list method and the map method and whether the concept mapping exercise shed additional light on student understanding.
Field Testing.
Student subjects. Twenty-one undergraduate students in microbiology (fall 2007) and 30 undergraduate students in genetics (spring 2008) participated in the activity. The prerequisite courses for microbiology are general biology and genetics. Genetics classes were added to serve as a control because in microbiology there were standard microbiology lectures that took place between day 1 and day 3 of the activity. Genetics classes did not receive these lectures and were used for comparison to isolate the impact of the activity.
Time spent on the Program Book. The amount of time microbiology students spent using the Program Book ranged from 35 to 129 minutes (mean = 71.7 minutes; standard deviation = 24.7 minutes; median = 65 minutes; n = 21). For genetics students, the amount of time ranged from 27 to 108 minutes (mean = 55.6 minutes; standard deviation = 16.9 minutes; median = 50 minutes; n = 38).
Coverage of the subdisciplines. Students in microbiology increased their combined coverage from 20 divisions preactivity to 23 postactivity (out of 26 possible divisions since Divisions S and T were combined for analysis). Students did not have to explicitly name a division in order for it to be considered as recognized. Instead, if students described research activities or applications encompassed by the division, that particular division was considered covered by the student.
Divisions with the most listings included: Division H (genetics and molecular biology), Division N (microbial ecology), and Division R (evolutionary and genomic microbiology).
Divisions added during the postactivity included: Division C (clinical microbiology), Division M (bacteriophage), Division W (microbiology education), and Division Y (public health).
Divisions with the largest increase postactivity included: Division D (bacteria of medical importance), Division N (microbial ecology), Division P (food microbiology), and Division Q (environmental and general applied microbiology).
Divisions that were excluded (pre- and postactivity) included: Division G (mycoplasmology), Division U (mycobacteriology), and Division X (molecular, cellular and general biology of eukaryotes). Eukaryotic microorganisms were mentioned, but based on the students’ descriptions, Division AA (free-living, symbiotic and parasitic protists) was judged to be a better fit.
FIG. 1. Divisional references by students in microbiology (n = 21) in fall 2007.
Students in genetics increased their combined coverage from 13 divisions preactivity to 25 divisions postactivity.
Divisions with the most listings included: Division E (immunology), Division I (general microbiology), Division J (cell and structural biology), Division S (DNA viruses), and Division T (RNA viruses).
Divisions that were never mentioned: Division V (clinical and diagnostic immunology) and Division X (molecular, cellular and general biology of eukaryotes).
FIG. 2. Divisional references by students in genetics (n = 30) in spring 2008.
Students’ awareness of the subdisciplines in microbiology increased significantly.
The number of subdisciplines listed before and after the activity was analyzed using a paired t test. The difference was shown to be significant in microbiology (t = 5.88, n = 21, P < 0.001) and in genetics (t = 8.53, n = 39, P < 0.001).
The correlation between the list method and the concept map method
A Pearson correlation (r) comparing the scorings of the list method and concept map method was determined for both microbiology and genetics courses. All data sets appear normally distributed, so a Pearson is appropriate. For the microbiology students, the Pearson correlation (r) is 0.458 (n = 58) and r2 = 0.21. An r2 of 0.21 indicates that 21% of the variability in one score can be explained by (predicted by) the variability in the other score. The correlation is significantly different from zero at P = 0.0003. For the genetics scores, the r is 0.479 (n = 37) with a P = 0.003, and r2 is 0.23. In both cases (microbiology and genetics), the correlation between the list method and the concept map method is not high (21% and 23%) but is statistically significant (both P values are below 5%).
 
FIG. 3. Students in microbiology and genetics reported whether they (1) strongly agree, (2) agree, (3) were neutral, (4) disagree, or (5) strongly disagree with the statement, "the activity helped me learn more about the subdisciplines of microbiology." Most students agreed that this activity helped them learn about the diversity of microbiology.Student Data.
While our correlation data suggested that there were weak correlations between the list method and the concept map method (21% for microbiology students and 23% for genetics students), the interesting cases shown below led us to believe that to holistically assess students, both methods should be considered. As shown, student #55 seemed to improve a great deal based on the list method; however, the concept map is not nearly as in depth as for student #20 whose list did not improve as dramatically after the activity. Growth in understanding of the diversity of microbiology was indicated for student #20, based on a comparison of the student’s pre- and postactivity concept maps. Interviews with future students participating in the activity are likely to shed additional light on the validity and specificity of the list and concept map assessments.
Student #20 preactivity list
Student #20 preactivity concept map
Student #20 postactivity list
Student #20 postactivity concept map
Student #55 preactivity list
Student #55 preactivity concept map
Student #55 postactivity list
Student #55 postactivity concept map
SUPPLEMENTARY MATERIALS
Possible Modifications.
· For us, the primary purpose of this activity was to introduce students to the process of constructing concept maps. This study skill is particularly important for students in microbiology because they need to organize and connect a massive amount of factual information. Since most undergraduate students have no previous exposure to microbiology and are relatively unaware of the range of the field, we designed this activity to engage students in the active investigation of this diversity and the process of organizing and connecting this information in a meaningful way. Therefore, using the ASM General Meeting Program Book and having the students construct a meeting itinerary are not as essential. Instructors can use the table of contents of a microbiology textbook or a list of the journal collections in the science library.
· While more investigation is required to bear this out, we suspect that having students browse through a physical copy of the ASM General Meeting Program Book may be more conducive to providing students with a high-level overview of the field than a piecemeal keyword search through electronic versions of the program or the ASM website.
· If computers are available, students could be given the option to create concept maps using CMap Tools. While the software is fairly intuitive, we suggest initially having the students transfer an existing handmade map to CMap Tools. Students are likely to focus on the mechanics of the software rather than the construction of their concept map if they create from scratch while also being introduced to the tool. For a brief demo of the software see: http://ctel.furman.edu/web/main/demos/cmap/cmap_demo.html.
References.
1. American Society for Microbiology. 2007. 107th General Meeting program book. American Society for Microbiology, Washington, DC.
2. Besterfield-Sacre, M., J. Gerchak, M. Lyons, L. J. Shuman, and H. Wolfe. 2004 Scoring concept maps: an integrated rubric for assessing engineering education. J. Eng. Educ. 93(2):105–115.
3. McClure, J. R., B. Sonak, and H. K. Suen. 1999. Concept map assessment of classroom learning: reliability, validity, and logistical practicality. J. Res. Sci. Teaching 36(4):475–492.
4. Novak, J. D., and A. J. Cañas. 2008. The theory underlying concept maps and how to construct and use them.
Florida Institute for Human and Machine Cognition, Pensacola, FL. http://cmap.ihmc.us/Publications/ResearchPapers/TheoryCmaps/TheoryUnderlyingConceptMaps.htm.
Acknowledgment.
The authors would like to thank Dr. Bill Blaker in the Biology Department at Furman University for his assistance in experimental design and data analysis and Dr. Larry Pace in the Psychology Department at Anderson University for reviewing the questionnaires.
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